December 2023 arXiv papers — page 126

A. S. Dmitriev, E. A. Dmitrieva, A. G. Panin

We have shown that the wave scattering by a soliton occurs in a peculiar way. The nonlinear interaction leads to the generation of waves with frequencies that are multiples of the frequency of the incident wave, minus the frequency of the soliton, while the soliton experiences growth due to the absorption of particles from the incoming wave. We propose an an

Mathieu d'Aquin

Building taxonomies is often a significant part of building an ontology, and many attempts have been made to automate the creation of such taxonomies from relevant data. The idea in such approaches is either that relevant definitions of the intension of concepts can be extracted as patterns in the data (e.g. in formal concept analysis) or that their extensio

Ilia M. Fradkin, Andrey A. Demenev, Anatoly V. Kovalchuk, Vladimir D. Kulakovskii

Grating couplers are widely used to couple waveguide modes with the far field. Their usefulness is determined not only by energy efficiency but also by additional supported functionality. In this paper, we demonstrate a plasmonic grating on a silicon nitride slab waveguide that couples both TE and TM waveguide modes with circularly polarized light in the far

Mathieu d'Aquin

In sufficiently complex tasks, it is expected that as a side effect of learning to solve a problem, a neural network will learn relevant abstractions of the representation of that problem. This has been confirmed in particular in machine vision where a number of works showed that correlations could be found between the activations of specific units (neurons)

Anton Nechaev, Mikhail Garasev, Vladimir Kocharovsky

We carry out particle-in-cell simulations of complex current sheets of the family of analytically found Vlasov--Maxwell equilibria that model a collisionless magnetopause and allow for arbitrary energy distributions and countercurrents of various particle component. We find that, depending on the parameters, i)~a weak small-scale bending-type instability is

A. G. Gamzatov, T. R. Arslanov, A. R. Kaul, Z. Z. Alisultanov

We investigated the effect of high pressure on the field dependences of magnetoresistance (MR) in La_{0.8}Ag_{0.1}MnO_{3} near the metal-insulator transition temperature. Our results showed that an increase in pressure results in a decrease in the magnitude of negative MR. At pressures $P\geqslant5.6$ GPa and magnetic fields up to 4 kOe, we observed a positi

Marco Buratti, Francesca Merola, Anamari Nakic, Christian Rubio-Montiel

We present some results on the harmonious colorings of the Levi graph of a $2$-design, focusing on Steiner $2$-designs. It is easily seen that the harmonious chromatic number of such a Levi graph is at least the number of points of the design: we study and construct Banff designs, that is, designs such that this lower bound is attained.

Yuekun Niu, Yu Ni, Haishan Zhang, Liang Qiu

We examine the orbital-selective Mott transition in the non-hybridized two-band Hubbard model using the dynamical mean-field theory. We find that the orbital-selective Mott transition could be quantitatively depicted by the {local two-qubit fidelity}. Furthermore, within the orbital-selective Mott phase, the combined characteristics of the two orbitals lead

Marianne Akian, Antoine Béreau, Stéphane Gaubert

Grigoriev and Podolskii (2018) have established a tropical analogue of the effective Nullstellensatz, showing that a system of tropical polynomial equations is solvable if and only if a linearized system obtained from a truncated Macaulay matrix is solvable. They provided an upper bound of the minimal admissible truncation degree, as a function of the degree

Zechuan Zhang, Zongxin Yang, Yi Yang

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is th

Augusto Cerqua, Marco Letta, Fiammetta Menchetti

Without a control group, the most widespread methodologies for estimating causal effects cannot be applied. To fill this gap, we propose the Machine Learning Control Method, a new approach for causal panel analysis that estimates causal parameters without relying on untreated units. We formalize identification within the potential outcomes framework and then

Cosimo Bambi

The theory of General Relativity has successfully passed a large number of observational tests without requiring any adjustment from its original version proposed by Einstein in 1915. The past 8 years have seen significant advancements in the study of the strong-field regime, which can now be tested with gravitational waves, X-ray data, and black hole imagin

Maomao Li, Yu Li, Tianyu Yang, Yunfei Liu

This paper presents a video inversion approach for zero-shot video editing, which models the input video with low-rank representation during the inversion process. The existing video editing methods usually apply the typical 2D DDIM inversion or naive spatial-temporal DDIM inversion before editing, which leverages time-varying representation for each frame t

Minye Wu, Tinne Tuytelaars

Adopting Neural Radiance Fields (NeRF) to long-duration dynamic sequences has been challenging. Existing methods struggle to balance between quality and storage size and encounter difficulties with complex scene changes such as topological changes and large motions. To tackle these issues, we propose a novel neural video-based radiance fields (NeVRF) represe

Chris Solomou

Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time. In this work, we show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior

Saraswati Pandey, B. K. Singh

In the hydrodynamical description of heavy-ion collisions, the elliptic flow $\mathrm{v_{2}}$ and triangular flow $\mathrm{v_{3}}$ are sensitive to the quadrupole deformation $\mathrm{\beta_{2}}$ of the colliding nuclei. We produce $\mathrm{v_{2}}$ and $\mathrm{v_{3}}$ ratios qualitatively and quantitatively in most-central Xe-Xe collisions at 5.44 TeV. By e

Yifan Sun, Jianquan Lu, Daniel W. C. Ho, Lulu Li

In this paper, we develop a new denial-of-service (DoS) estimator, enabling defenders to identify duration and frequency parameters of any DoS attacker, except for three edge cases, exclusively using real-time data. The key advantage of the estimator lies in its capability to facilitate security control in a wide range of practical scenarios, even when the a

Per Pettersson, Eirik Keilegavlen, Tor Harald Sandve, Sarah Gasda

Subsurface storage of CO$_2$ is an important means to mitigate climate change, and to investigate the fate of CO$_2$ over several decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges as their effects on storage operations are uncertain due to limi

T. Völkl, A. Aharon-Steinberg, T. Holder, E. Alpern

Attaining viable thermoelectric cooling at cryogenic temperatures is of major fundamental and technological interest for novel electronics and quantum materials applications. In-device temperature control can provide a more efficient and precise thermal environment management as compared to the conventional global cooling. Here we develop nanoscale cryogenic

Jiun Tian Hoe, Xudong Jiang, Chee Seng Chan, Yap-Peng Tan

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to

Bach Nguyen Gia, Chanh Minh Tran, Tho Nguyen Duc, Tan Phan Xuan

Graph-based Light Field coding using the concept of super-rays is powerful to exploit signal redundancy along irregular shapes and achieves good energy compaction, compared to rectangular block -based approaches. However, its main limitation lies in the high time complexity for eigen-decomposition of each super-ray local graph, a high number of which can be

Francisco Braun, Leonardo P. C. da Cruz, Joan Torregrosa

We consider piecewise quadratic perturbations of centers of piecewise quadratic systems in two zones determined by a straight line through the origin. By means of expansions of the displacement map, we are able to find isolated zeros of it, without dealing with the unsurprising difficult integrals inherent in the usual averaging approach. We apply our techni

Artur Movsesyan, Lucas V. Besteiro, Zhiming Wang, Alexander O. Govorov

In this work, we use artificial neural networks (ANNs) to recognize the material composition, sizes of nanoparticles and their concentrations in different media with high accuracy, solely from the absorbance spectrum of a macroscopic sample. We construct ANNs operating in the following two schemes. The first scheme is designed to recognize the dimensions and

Sándor Jenei

The representation theorem for odd or even involutive FLe-chains by bunches of layer groups, as discussed in [10], is redefined to demonstrate a more straightforward constructional relationship between odd or even involutive FLe-chains and bunches of layer groups, bypassing the intermediary stage of layer algebras. By leveraging this redefined theorem, it is

Hemant Masal, Subhash Kendre, Hemant Bhate

We establish some functional identities of theta functions, an elementary proof of classical fourth-order identities, Landen transformations, and q series from the eigenvectors of the discrete Fourier transform. Also, we derive connection between Rogers-Ramanujan type identity and theta function identity.

Alberto González-Sanz, Michel Groppe, Axel Munk

The inverse optimal transport problem is to find the underlying cost function from the knowledge of optimal transport plans. While this amounts to solving a linear inverse problem, in this work we will be concerned with the nonlinear inverse problem to identify the cost function when only a set of marginals and its corresponding optimal values are given. We

Yongheng Deng, Ziqing Qiao, Ju Ren, Yang Liu

While large language models (LLMs) are empowered with broad knowledge, their task-specific performance is often suboptimal. It necessitates fine-tuning LLMs with task-specific data, but such data may be inaccessible due to privacy concerns. In this paper, we propose a novel approach to enhance LLMs with smaller language models (SLMs) that are trained on clie

Shiran Ziv Sharabani, Elad Livnat, Maia Abuchalja, Noa Haphiloni

Two-dimensional shape-morphing networks are common in biological systems and have garnered attention due to their nontrivial physical properties that emanate from their cellular nature. Here, we present the fabrication and characterization of inhomogeneous shape-morphing networks composed of thermoresponsive microfibers. By strategically positioning fibers w

Xenia Dimitrakopoulou

By $p$-adically interpolating the branching law for the spherical pair $\left(U_n, U_{n+1} \times U_{n}\right)$ of definite unitary groups, we construct a $p$-adic $L$-function attached to cohomological automorphic representations of $U_{n+1} \times U_{n}$. Under a further multiplicity one assumption, we extend the construction to Coleman families. Our $p$-a

Rubén Ballester, Carles Casacuberta, Sergio Escalera

This survey provides a comprehensive exploration of applications of Topological Data Analysis (TDA) within neural network analysis. Using TDA tools such as persistent homology and Mapper, we delve into the intricate structures and behaviors of neural networks and their datasets. We discuss different strategies to obtain topological information from data and

Vishwajeet Kumar, Arnab Pal, Ohad Shpielberg

Thermal activation of a particle from a deep potential trap follows the Arrhenius law. Recently, this result was generalized for interacting diffusive particles in the trap, revealing two universality classes -- the Arrhenius class and the excluded volume class. The result was demonstrated with the aid of numerical analysis. Here, we present a perturbative h

Eyal Buks

We experimentally study an unequally-spaced optical comb (USOC), which is generated by a unidirectional fiber loop laser operated at low temperatures. The underlying mechanism responsible for USOC formation is explored using both close and open loop measurements. The role played by dispersion is investigated using radio frequency spectrum measurements. By in

Langyu Li, Daoyi Dong, Yu Pan

Quantum annealers, coherent Ising machines and digital Ising machines for solving quantum-inspired optimization problems have been developing rapidly due to their near-term applications. The numerical solvers of the digital Ising machines are based on traditional computing devices. In this work, we propose a fast and efficient solver for the Ising optimizati

Milan Lopuhaä-Zwakenberg

Fault tree (FT) analysis is a prominent risk assessment method in industrial systems. Unreliability is one of the key safety metrics in quantitative FT analysis. Existing algorithms for unreliability analysis are based on binary decision diagrams, for which it is hard to give time complexity guarantees beyond a worst-case exponential bound. In this paper, we

Lei Zhang, Paulo C. C. Freire, Alessandro Ridolfi, Zhichen Pan

We report on a comprehensive multi-wavelength study of the pulsars in the globular cluster (GC) M5, including the discovery of M5G, a new compact non-eclipsing "black widow" pulsar. Thanks to the analysis of 34 years of radio data taken with the FAST and Arecibo telescopes, we obtained new phase-connected timing solutions for four pulsars in the clusters and

Xin Tan, Bowei Zou, Ai Ti Aw

Universal fact-checking systems for real-world claims face significant challenges in gathering valid and sufficient real-time evidence and making reasoned decisions. In this work, we introduce the Open-domain Explainable Fact-checking (OE-Fact) system for claim-checking in real-world scenarios. The OE-Fact system can leverage the powerful understanding and r

Joshua Pilipovsky, Panagiotis Tsiotras

The theory of covariance control and covariance steering (CS) deals with controlling the dispersion of trajectories of a dynamical system, under the implicit assumption that accurate prior knowledge of the system being controlled is available. In this work, we consider the problem of steering the distribution of a discrete-time, linear system subject to exog

Yang Zhang, Huilin Pan, Mingying Li, An Wang

Despite the successful application of convolutional neural networks (CNNs) in object detection tasks, their efficiency in detecting faults from freight train images remains inadequate for implementation in real-world engineering scenarios. Existing modeling shortcomings of spatial invariance and pooling layers in conventional CNNs often ignore the neglect of

Francesco Di Fiore, Laura Mainini

The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Multifidelity Bayesian methods (MFBO) allow to include costly high-fidelity responses for a sub-selection of queries only, and use fast lower-fidelity models to accelerate the optimiz

Yunheng Li, Zhongyu Li, Shanghua Gao, Qilong Wang

Effectively modeling discriminative spatio-temporal information is essential for segmenting activities in long action sequences. However, we observe that existing methods are limited in weak spatio-temporal modeling capability due to two forms of decoupled modeling: (i) cascaded interaction couples spatial and temporal modeling, which over-smooths motion mod

Shuyang Jiang, Kung Yao

In this paper, the recursive least squares (RLS) algorithm is considered in the sparse system identification setting. The cost function of RLS algorithm is regularized by a $p$-norm-like ($0 \leq p \leq 1$) constraint of the estimated system parameters. In order to minimize the regularized cost function, we transform it into a penalized maximum likelihood (M

Hye-Bin Shin, Kang Yin, Seong-Whan Lee

In the quest for efficient neural network models for neural data interpretation and user intent classification in brain-computer interfaces (BCIs), learning meaningful sparse representations of the underlying neural subspaces is crucial. The present study introduces a sparse multitask learning framework for motor imagery (MI) and motor execution (ME) tasks,

Álvaro Cartea, Gerardo Duran-Martin, Leandro Sánchez-Betancourt

This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online learning Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient Bayesian procedure for online training of neural netw

Yuanwang Yang, Qiao Feng, Yu-Kun Lai, Kun Li

Rendering 3D human appearance from a single image in real-time is crucial for achieving holographic communication and immersive VR/AR. Existing methods either rely on multi-camera setups or are constrained to offline operations. In this paper, we propose R2Human, the first approach for real-time inference and rendering of photorealistic 3D human appearance f

Seonghak Kim, Baisheng Yan

In this paper we provide a different approach for existence of the variational solutions of the gradient flows associated to functionals on Sobolev spaces studied in \cite{BDDMS20}. The crucial condition is the convexity of the functional under which we show that the variational solutions coincide with the solutions generated by the nonlinear semigroup assoc

Junjian Mi, Jialin Li, Miaocong Li, Sheng Xu

The topology between Bloch states in reciprocal space has attracted tremendous attention in recent years. The quantum geometry of the band structure is composed of quantum metric as real part and berry curvature as imaginary part. While the Berry curvature, the Berry curvature dipole and Berry connection polarizability have been recently revealed by the firs

Shuai Li, Minghan Li, Pengfei Wang, Lei Zhang

Recently, a few open-vocabulary methods have been proposed by employing a unified architecture to tackle generic segmentation and detection tasks. However, their performance still lags behind the task-specific models due to the conflict between different tasks, and their open-vocabulary capability is limited due to the inadequate use of CLIP. To address thes

M. Firat Arikan

It is known that the folded sum of two contact mapping tori whose fibers are compact exact symplectic manifolds having a common convex boundary (called the ``fold'') admits a cooriented contact structure compatible with the obvious fibration map onto the circle. Here we first provide an alternative bundle-theoretical construction of such a ``folded'' contact

William Wei Wang, Dongqi Han, Xufang Luo, Yifei Shen

Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years. A major challenge is task open-endedness. In practice, robots often need to perform tasks with novel goals that are multifaceted, dynamic, lack a definitive "end-state", and were not encountered during training. To tackle this prob

Zhihang Yuan, Yuzhang Shang, Yue Song, Dawei Yang

In this paper, we introduce a new post-training compression paradigm for Large Language Models (LLMs) to facilitate their wider adoption. We delve into LLM weight low-rank decomposition, and find that the challenges of this task stem from (1) the distribution variance in the LLM activations and (2) the sensitivity difference among various kinds of layers. To

Alekseeva L. S., Nokhrin A. V., Yunin P. A., Nazarov A. A.

Oxide Y2.5Nd0.5Al5O12 (YAG:Nd) with garnet structure was synthesized in the powder and ceramics forms. Fine-grained YAG:Nd ceramics with a relative density of ~99% were obtained by the Spark Plasma Sintering method (SPS). The radiation resistance of ceramics was studied under irradiation with swift Xe-ions (E = 146 MeV). A gradient defect structure is formed

Daniele Narducci, Xiangyu Wu, Isabella Boventer, Jo De Boeck

This study introduces a method for the characterization of the magnetoelectric coupling in nanoscale Pb(Zr,Ti)O3/CoFeB thin film composites based on propagating spin-wave spectroscopy. Finite element simulations of the strain distribution in the devices indicated that the magnetoelastic effective field in the CoFeB waveguides was maximized in the Damon - Esh

Chanon Puttanawarut, Panu Looareesuwan, Romen Samuel Wabina, Prut Saowaprut

Survival analysis is a widely known method for predicting the likelihood of an event over time. The challenge of dealing with censored samples still remains. Traditional methods, such as the Cox Proportional Hazards (CPH) model, hinge on the limitations due to the strong assumptions of proportional hazards and the predetermined relationships between covariat

Peter J. Cho, Keunyoung Jeong, Junyeong Park

Assuming the Hasse--Weil conjecture and the generalized Riemann hypothesis for the $L$-functions of the elliptic curve, we give an upper bound of the average analytic rank of elliptic curves over the number field with a level structure such that the corresponding compactified moduli stack is representable by the projective line.

Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan

We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper, we present I'm-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minima

Haozhao Li, Linwei Zhang, Kai Zheng

In this paper, we show that the Calabi flow can be extended as long as the $L^p$ scalar curvature is uniformly bounded for some $p>n$, and on a compact extremal K\"ahler manifold the Calabi flow with uniformly bounded $L^p(p>n)$ scalar curvature exists for all time and converges exponentially fast to an extremal K\"ahler metric.

Joshua Ball

In the realm of digital audio processing, Voice Activity Detection (VAD) plays a pivotal role in distinguishing speech from non-speech elements, a task that becomes increasingly complex in noisy environments. This paper details the development and implementation of a VAD system, specifically engineered to maintain high accuracy in the presence of various amb

Seo-Hyun Lee, Young-Eun Lee, Soowon Kim, Byung-Kwan Ko

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly e

V. N. Chuvil'deev, M. Y. Gryaznov, S. V. Shotin, A. V. Nokhrin

Superplastic behavior of ultrafine-grained (UFG) Al-6Mg-0.12Sc-0.10Zr-0.1X alloys, where X = Yb (Alloy #1-Yb), Er (Alloy #2-Er), and Hf (Alloy #3-Hf), has been studied. The total content of Sc, Zr, Yb, Er, Hf in the alloys was 0.32 wt.% (0.117-0.118 at.%). The alloys used for benchmarking were Al-6Mg-0.12Sc-0.20Zr (Alloy #4-Zr) and Al-6Mg-0.22Sc-0.10Zr (Allo

Y. G. Pogorelov, V. M. Loktev

We consider electronic spectra of graphene nanotubes and their perturbation by impurity atoms absorbed at different positions on nanotube surfaces, within the framework of Anderson hybrid model. A special attention is given to the cases when Dirac-like 1D modes appear in the nanotube spectrum and their hybridization with localized impurity states produces, a

Linyi Zhu, Weifeng Lin, Monika Kluzek, Jadwiga Miotla-Zarebska

Osteoarthritis (OA) is a widespread, debilitating joint disease associated with articular cartilage degradation. It is driven via mechano-inflammatory catabolic pathways, presumed up-regulated due to increased shear stress on the cartilage-embedded chondrocytes, that lead to tissue degeneration. Here we demonstrate that the up-regulation of the matrix metall

Shenglin Huang, Zequn He, Nicolas Dirr, Johannes Zimmer

Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It has been increasingly informed by physics, and more recently by thermodynamics, to further uncover the thermodynamic structure underlying the evolution equations, i.e., the thermod

Benjamin T. Liberles, Jason A. Dittmann, Stephen M. Elardo, Sarah Ballard

Previous work has established the enhanced occurrence of compact systems of multiple small exoplanets around metal-poor stars. Understanding the origin of this effect in the planet formation process is a topic of ongoing research. Here we consider the radii of planets residing in systems of multiple transiting planets, compared to those residing in single-tr

Yusuke Nakamura, Kohsuke Shibata

We prove the precise inversion of adjunction formula for finite linear group quotients of complete intersection varieties defined by semi-invariant equations. As an application, we prove the semi-continuity of minimal log discrepancies for them. These results extend the results in our first paper, where we prove the same results for complete intersection var

Rui Ye, Xinyu Zhu, Jingyi Chai, Siheng Chen

Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way. However, data heterogeneity significantly limits the performance of current FL methods. In this paper, we propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative con

Massimo A. Picardello, Maura Salvatori, Wolfgang Woess

We consider the open unit disk $\mathbb{D}$ equipped with the hyperbolic metric and the associated hyperbolic Laplacian $\mathfrak{L}$. For $\lambda \in \mathbb{C}$ and $n \in \mathbb{N}$, a $\lambda$-polyharmonic function of order $n$ is a function $f: \mathbb{D} \to \mathbb{C}$ such that $(\mathfrak{L}- \lambda \, I)^n f = 0$. If $n =1$, one gets $\lambda$

Kelley Ann Yohe

Recommender systems have gained increasing attention to personalise consumer preferences. While these systems have primarily focused on applications such as advertisement recommendations (e.g., Google), personalized suggestions (e.g., Netflix and Spotify), and retail selection (e.g., Amazon), there is potential for these systems to benefit from a more global

Chhavi Maheshwari

Hosting about 50 million songs and 4 billion playlists, there is an enormous amount of data generated at Spotify every single day - upwards of 600 gigabytes of data (harvard.edu). Since the algorithms that Spotify uses in recommendation systems is proprietary and confidential, code for big data analytics and recommendation can only be speculated. However, it

Yi Wang, Jian Ma, Ruizhi Shao, Qiao Feng

The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body structures. As a result, these methods are unable to arbitrarily and separately change and edit the body and clothing of the human. In this paper, we propose a text-driven layered 3

Tianyi Zhang, Kishore Kasichainula, Yaoxin Zhuo, Baoxin Li

Conventional super-resolution methods suffer from two drawbacks: substantial computational cost in upscaling an entire large image, and the introduction of extraneous or potentially detrimental information for downstream computer vision tasks during the refinement of the background. To solve these issues, we propose a novel transformer-based algorithm, Selec

Yifan Cheng, Cheng Li

This article introduces novel and practicable Bayesian factor analysis frameworks that are computationally feasible for moderate to large spatiotemporal data. Previous Bayesian analysis of spatiotemporal data has utilized a Bayesian factor model with separable temporal latent factors and spatial factor loadings, along with stick-breaking process priors on th

Alexander Kang-Jun Toh, McCoy W. Lim, T. S. Suraj, Xiaoye Chen

Ambient magnetic skyrmions stabilized in multilayer nanostructures are of immense interest due to their relevance to magnetic tunnel junction (MTJ) devices for memory and unconventional computing applications. However, existing skyrmionic nanostructures built using conventional metallic or oxide multilayer nanodots are unable to concurrently fulfill the requ

Min Zhang, Runze Mao, Han Li, Ruixin Yang

Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increasingly stringent environmental regulations. Numerous numerical simulations with different combustion models and mesh resolutions have been conducted on laboratory-scale flames to f

Zhengxue Wang, Zhiqiang Yan, Jian Yang

Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure. However, since the structure of LR depth is usually blurry, only considering spatial domain is not very sufficie

Wenju Xu, Chengjiang Long, Yongwei Nie, Guanghui Wang

In this paper, we propose a novel framework named DRL-CPG to learn disentangled latent representation for controllable person image generation, which can produce realistic person images with desired poses and human attributes (e.g., pose, head, upper clothes, and pants) provided by various source persons. Unlike the existing works leveraging the semantic mas

Praneeta Immadisetty, Pooja Rajesh, Akshita Gupta, Anala M R

The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which

Yuyang Deng, Ni Zhao, Xin Huang

Since its launch, ChatGPT has achieved remarkable success as a versatile conversational AI platform, drawing millions of users worldwide and garnering widespread recognition across academic, industrial, and general communities. This paper aims to point a portrait of early GPT users and understand how they evolved. Specific questions include their topics of i

Hongwei Hou, Xuan He, Tianhao Fang, Xinping Yi

This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model the sparsity in the d

Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen

The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable models introduces challenges, particularly in increased latency and carbon emissions, which are antithetical to th

Muhammad Abdullah Naeem, Miroslav Pajic

Performance of ordinary least squares(OLS) method for the \emph{estimation of high dimensional stable state transition matrix} $A$(i.e., spectral radius $\rho(A)<1$) from a single noisy observed trajectory of the linear time invariant(LTI)\footnote{Linear Gaussian (LG) in Markov chain literature} system $X_{-}:(x_0,x_1, \ldots,x_{N-1})$ satisfying \begin{equ

Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff

Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in biophysical systems to modeling financial instruments. Assuming that the underlying dynamical process obeys a $d$-dimensional stochastic differential equation of the form $$\mathrm{

Li Shen, Yuning Wei, Yangzhu Wang, Hongguang Li

With the development of Internet of Things (IoT) systems, precise long-term forecasting method is requisite for decision makers to evaluate current statuses and formulate future policies. Currently, Transformer and MLP are two paradigms for deep time-series forecasting and the former one is more prevailing in virtue of its exquisite attention mechanism and e

Ivan Ranđelović, Kinga Nyíri, Gergely Koppány, Marcel Baranyi

Mutated genes may lead to cancer development in numerous tissues. While more than 600 cancer-causing genes are known today, some of the most widespread mutations are connected to the RAS gene: RAS mutations are found in approximately 25% of all human tumors. Specifically, KRAS mutations are involved in the three most lethal cancers in U.S.: pancreatic ductal

Hyun Ryu, Sunjae Yoon, Hee Suk Yoon, Eunseop Yoon

Data augmentation is a crucial component in training neural networks to overcome the limitation imposed by data size, and several techniques have been studied for time series. Although these techniques are effective in certain tasks, they have yet to be generalized to time series benchmarks. We find that current data augmentation techniques ruin the core inf

Davar Khoshnevisan, Kunwoo Kim, Carl Mueller

We study small-ball probabilities for the stochastic heat equation with multiplicative noise in the moderate-deviations regime. We prove the existence of a small-ball constant and related it to other known quantities in the literature. These small-ball estimates are known to imply Chung-type laws of the iterated logarithm (LIL) at typical spatial points; the

Siddhartha Banerjee, Jolly Xavier

The generation of the localized surface plasmon resonance (LSPR) on the surface of plasmonic structures in the nanoscale has paved the way for advanced biosensing, surpassing the conventional detection limits. The electric field enhancement (electromagnetic hot spots) between two plasmonic nano structures at close quarters produces hot electrons with a chang

Takuya Hiraoka

Reinforcement learning (RL) methods with a high replay ratio (RR) and regularization have gained interest due to their superior sample efficiency. However, these methods have mainly been developed for dense-reward tasks. In this paper, we aim to extend these RL methods to sparse-reward goal-conditioned tasks. We use Randomized Ensemble Double Q-learning (RED

Junyi Yang, Weifeng Zhu, Shu Sun, Xiaofeng Li

This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission. We propose a novel deep learning based framework where the procedures of pilot design, channel feedback, and hybrid beamforming are realized

Bertrand Ottino-Loffler, Gabriel Victora

A central feature of vertebrate immune response is affinity maturation, wherein antibody-producing B cells undergo evolutionary selection in microanatomical structures called germinal centers, which form in secondary lymphoid organs upon antigen exposure. While it has been shown that the median B cell affinity dependably increases over the course of maturati

Jayabrata Chowdhury, Venkataramanan Shivaraman, Suresh Sundaram, P B Sujit

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather cond

Kunyang Lin, Yufeng Wang, Peihao Chen, Runhao Zeng

Learning optimal behavior policy for each agent in multi-agent systems is an essential yet difficult problem. Despite fruitful progress in multi-agent reinforcement learning, the challenge of addressing the dynamics of whether two agents should exhibit consistent behaviors is still under-explored. In this paper, we propose a new approach that enables agents

Ke Xiao, Jing Li, Rui Ma, Chen-Xu Wu

Due to the potential applications in biomedical engineering, it becomes more and more important to understand the process of engulfment and internalization of nanoparticles (NPs) by cell membranes. Despite the fact that the interaction between cell membranes and passive particles has been widely studied, the interaction between cell membranes and self-propel

Chul-Hwan Kim, Jeong-Eun Lee, Carlos Contreras Peña, Doug Johnstone

Protostellar outflows often present a knotty appearance, providing evidence of sporadic accretion in stellar mass growth. To understand the direct relation between mass accretion and ejection, we analyze the contemporaneous accretion activity and associated ejection components in B335. B335 has brightened in the mid-IR by 2.5 mag since 2010, indicating incre

Md. Zarif Ul Alam, Md Saiful Islam, Ehsan Hoque, M Saifur Rahman

Parkinson's disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. Timely diagnosis and treatment can improve the quality of life for PD patients. However, access to clinical diagnosis is limited in low and middle income countries (LMICs). Therefore, development of automated screening tools for PD can have a huge socia

Toma Sakurai, Satoshi Ohshima, Takahiro Katagiri, Toru Nagai

Recently, computers have diversified architectures. To achieve high numerical calculation software performance, it is necessary to tune the software according to the target computer architecture. However, code optimization for each environment is difficult unless it is performed by a specialist who knows computer architectures well. By applying autotuning (A

Zhuolin Xu, Qiushi Li, Shin Hwei Tan

The rapid evolution of Web UI incurs time and effort in UI test maintenance. Prior techniques in Web UI test repair focus on locating the target elements on the new Webpage that match the old ones so that the corresponding broken statements can be repaired. These techniques usually rely on prioritizing certain attributes (e.g., XPath) during matching where t

Xu Zhang, Hao Li, Mang Ye

Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unresolved noise, existing methods cannot maintain a stable performance when the noise increases. In this paper, we present a novel Negative Pre-aware Cross-modal (NPC) matching solution

Gi-Chol Cho, Chikako Idegawa, Rie Inumiya

We examine a complex singlet scalar extension of the Standard Model (CxSM) with an extra singlet fermion. Both the singlet scalar and fermion are dark matter (DM) candidates. It is known that although the scalar potential in the CxSM can realize strong first-order electroweak phase transition, the scalar DM included in the model gives only a tiny amount of t

Nicholas Skjellum, Mohamed Shaban, Muhammad Ismail

Quantum information technology has the potential to revolutionize computing, communications, and security. To fully realize its potential, quantum processors with millions of qubits are needed, which is still far from being accomplished. Thus, it is important to establish quantum networks to enable distributed quantum computing to leverage existing and near-

Mohamed Shaban, Muhammad Ismail

Quantum Internet signifies a remarkable advancement in communication technology, harnessing the principles of quantum entanglement and superposition to facilitate unparalleled levels of security and efficient computations. Quantum communication can be achieved through the utilization of quantum entanglement. Through the exchange of entangled pairs between tw